Can AI enhance VR training? A systematic review of AI-VR training research.
Michael DeFabiis1, Kevin Askew1, Valerie Sessa1
1 Montclair State University
Introduction
Very little research exists highlighting the presence or absence of training best practices.
RQ 1: Are the current VR and AI training programs utilizing best practices from the science of training?
RQ 2: Are current studies on AI-enhanced VR training programs utilizing a proper control group?
RQ 3: Do current studies on AI-enhance VR training programs have a sufficient number of people to make inferences about their effectiveness?
RQ 4: What are the preliminary findings? Does adding AI to VR training result in a better experience for the trainee, greater learning, or better on-the-job performance than regular VR training?
Methods
Results
Discussion
| Research Question | Guidelines for Future Research |
|---|---|
| 1a. Are learning objectives created and clarified to the reader? | Create clear learning objectives by including a behavior and criterion. The condition is often implied for AI-VR training articles, but it can be helpful to include. |
| 1b. Is the training delivered in a way that builds trainees’ belief in their ability to learn and display trained skills (self-efficacy)? | Measure self-efficacy directly using a self-efficacy assessment. |
| 1c. Does the training encourage trainees to participate in training to learn rather than to appear capable (promotes a learning orientation)? | Include effective feedback mechanisms, encourage error making, and create training aspects that focus on learning rather than performance. Assessing participants inside of a AI-VR training program can inhibit learning outcomes. |
| 1d. Does the training engage trainees and build their interest? | Measure engagement directly using an engagement assessment. |
| 1e. Does the training utilize a valid training strategy and design? This involves providing information, giving demonstrations of good and bad behaviors, allowing opportunities to practice, and providing meaningful feedback. | AI-VR training programs should strive to incorporate all of these aspects. |
| 1f. Does the training allow trainees to use the same cognitive processes that they will have to in the environment this learning should transfer to? | Create immersive training programs that replicate real life tasks and scenarios. |
| 1g. Does the training keep trainees’ attention by allowing trainees to monitor their progress toward goals? | AI-VR training programs can allow trainees’ to monitor progress via user interface (UI) elements such as maps and progress bars. Trainings programs can also implement checkpoints and endpoints with visual / haptic feedback (success screens, animations, sounds, etc.) |
| 1h. Does the training encourage trainees to make errors? | Provide instant feedback and additional opportunities for practice to encourage errors. Do not assess participant performance inside of the training program. |
| 1i. Does the training provide sufficient structure to trainees when allowing them to make decisions about their learning experience? | Build game elements that allow trainees to select training courses or a preferred order of courses. Create scenarios that allow autonomous movement and decision making. Use algorithms that select training scenarios based on participant’s training decisions. |
| 1j. Does the training simulation increase psychological fidelity (e.g. job-relevant, technology used fits the task)? | Ensure that the training programs are job-relevant, have good task-technology fit, and are immersive enough to boost user presence. |
| 1k. Does the training use established learning / outcome taxonomies (e.g. affective, cognitive, and/or behavioral indicators)? | Always include cognitive and / or behavioral indicators. Include reaction measures beyond engagement and self-efficacy to help provide more information to readers. |
| Note: | |
| This table highlights guidelines for achieving each of the training best practices suggested by Salas (2012). |